Initial data:
public class Stats {
int passesNumber;
int tacklesNumber;
public Stats(int passesNumber, int tacklesNumber) {
this.passesNumber = passesNumber;
this.tacklesNumber = tacklesNumber;
}
public int getPassesNumber() {
return passesNumber;
}
public void setPassesNumber(int passesNumber) {
this.passesNumber = passesNumber;
}
public int getTacklesNumber() {
return tacklesNumber;
}
public void setTacklesNumber(int tacklesNumber) {
this.tacklesNumber = tacklesNumber;
}
}
Map<String, List<Stats>> statsByPosition = new HashMap<>();
statsByPosition.put("Defender", Arrays.asList(new Stats(10, 50), new Stats(15, 60), new Stats(12, 100)));
statsByPosition.put("Attacker", Arrays.asList(new Stats(80, 5), new Stats(90, 10)));
I need to calculate an average of Stats by position. So result should be a map with the same keys, however values should be aggregated to single Stats object (List should be reduced to single Stats object)
{
"Defender" => Stats((10 + 15 + 12) / 3, (50 + 60 + 100) / 3),
"Attacker" => Stats((80 + 90) / 2, (5 + 10) / 2)
}
I don't think there's anything new in Java8 that could really help in solving this problem, at least not efficiently.
If you look carefully at all new APIs, then you will see that majority of them are aimed at providing more powerful primitives for working on single values and their sequences - that is, on sequences of double, int, ? extends Object, etc.
For example, to compute an average on sequence on double, JDK introduces a new class - DoubleSummaryStatistics which does an obvious thing - collects a summary over arbitrary sequence of double values.
I would actually suggest that you yourself go for similar approach: make your own StatsSummary class that would look along the lines of this:
// assuming this is what your Stats class look like:
class Stats {
public final double a ,b; //the two stats
public Stats(double a, double b) {
this.a = a; this.b = b;
}
}
// summary will go along the lines of:
class StatsSummary implements Consumer<Stats> {
DoubleSummaryStatistics a, b; // summary of stats collected so far
StatsSummary() {
a = new DoubleSummaryStatistics();
b = new DoubleSummaryStatistics();
}
// this is how we collect it:
#Override public void accept(Stats stat) {
a.accept(stat.a); b.accept(stat.b);
}
public void combine(StatsSummary other) {
a.combine(other.a); b.combine(other.b);
}
// now for actual methods that return stuff. I will implement only average and min
// but rest of them are not hard
public Stats average() {
return new Stats(a.getAverage(), b.getAverage());
}
public Stats min() {
return new Stats(a.getMin(), b.getMin());
}
}
Now, above implementation will actually allow you to express your proper intents when using Streams and such: by building a rigid API and using classes available in JDK as building blocks, you get less errors overall.
However, if you only want to compute average once somewhere and don't need anything else, coding this class is a little overkill, and here's a quick-and-dirty solution:
Map<String, Stats> computeAverage(Map<String, List<Stats>> statsByPosition) {
Map<String, Stats> averaged = new HashMap<>();
statsByPosition.forEach((position, statsList) -> {
averaged.put(position, averageStats(statsList));
});
return averaged;
}
Stats averageStats(Collection<Stats> stats) {
double a, b;
int len = stats.size();
for(Stats stat : stats) {
a += stat.a;
b += stat.b;
}
return len == 0d? new Stats(0,0) : new Stats(a/len, b/len);
}
There is probably a cleaner solution with Java 8, but this works well and isn't too complex:
Map<String, Stats> newMap = new HashMap<>();
statsByPosition.forEach((key, statsList) -> {
newMap.put(key, new Stats(
(int) statsList.stream().mapToInt(Stats::getPassesNumber).average().orElse(0),
(int) statsList.stream().mapToInt(Stats::getTacklesNumber).average().orElse(0))
);
});
The functional forEach method lets you iterate over every key value pair of your given map.
You just put a new entry in your map for the averaged values. There you take the key you have already in your given map. The new value is a new Stats, where the arguments for the constructor are calculated directly.
Just take the value of your old map, which is the statsList in the forEach function, map the values from the given stats to Integer value with mapToInt and use the average function.
This function returns an OptionalDouble which is nearly the same as Optional<Double>. Preventing that anything didn't work, you use its orElse() method and pass a default value (like 0). Since the average values are double you have to cast the value to int.
As mentioned, there doubld probably be a even shorter version, using reduce.
You might as well use custom collector. Let's add the following methods to Stats class:
public Stats() {
}
public void accumulate(Stats stats) {
passesNumber += stats.passesNumber;
tacklesNumber += stats.tacklesNumber;
}
public Stats combine(Stats acc) {
passesNumber += acc.passesNumber;
tacklesNumber += acc.tacklesNumber;
return this;
}
#Override
public String toString() {
return "Stats{" +
"passesNumber=" + passesNumber +
", tacklesNumber=" + tacklesNumber +
'}';
}
Now we can use Stats in collect method:
System.out.println(statsByPosition.entrySet().stream().collect(
Collectors.toMap(
entity -> entity.getKey(),
entity -> {
Stats entryStats = entity.getValue().stream().collect(
Collector.of(Stats::new, Stats::accumulate, Stats::combine)
); // get stats for each map key.
// get average
entryStats.setPassesNumber(entryStats.getPassesNumber() / entity.getValue().size());
// get average
entryStats.setTacklesNumber(entryStats.getTacklesNumber() / entity.getValue().size());
return entryStats;
}
))); // {Attacker=Stats{passesNumber=85, tacklesNumber=7}, Defender=Stats{passesNumber=12, tacklesNumber=70}}
If java-9 is available and StreamEx, you could do :
public static Map<String, Stats> third(Map<String, List<Stats>> statsByPosition) {
return statsByPosition.entrySet().stream()
.collect(Collectors.groupingBy(e -> e.getKey(),
Collectors.flatMapping(e -> e.getValue().stream(),
MoreCollectors.pairing(
Collectors.averagingDouble(Stats::getPassesNumber),
Collectors.averagingDouble(Stats::getTacklesNumber),
(a, b) -> new Stats(a, b)))));
}
How can I write the following method in Java 8 streams? I couldn't find a way to do it. This is my code:
public static List<ObjectB> getFilteredList(List<ObjectA> list, LocalTime startTime, LocalTime endTime, int quantity) {
List<ObjectA> objectAList = new LinkedList<>();
List<ObjectB> objectBList = new LinkedList<>();
for (ObjectA object : list) {
if (object.getDateTime().toLocalTime().isAfter(startTime) && object.getDateTime().toLocalTime().isBefore(endTime)) {
objectAList.add(object);
}
}
for (ObjectA objectA : objectAList) {
int total = 0;
for (ObjectA object : list) {
if (object.getDateTime().toLocalDate().equals(objectA.getDateTime().toLocalDate())) {
total += object.getQuantity();
}
}
if (total > quantity) {
objectBList.add(new ObjectB(objectA.getDateTime(), objectA.getDescription(), objectA.getQuantity(), true));
} else {
objectBList.add(new ObjectB(objectA.getDateTime(), objectA.getDescription(), objectA.getQuantity(), false));
}
}
return objectBList;}
I have a list of objects with two fields: date and quantity. I need to return a list with one object for each date, but with one more feild - boolean, which should be true if the total sum of all quantites per day is more than 16, and false if it's not.
Let's do this step-by-step.
for (ObjectA object : list) {: a for loop is usually replaced with stream(), so start with list.stream().
if (...) {: condition is usually replaced with filter(), so continue with .filter(object -> object.getDateTime()...)
objectAList.add(object);: adding the results to the container is usually replaced with collect(). You are using LinkedList(), but any other List would be fine here, so we will simply use collect(Collectors.toList()).
So here's first loop:
List<ObjectA> objectAList = list.stream()
.filter(object -> object.getDateTime().toLocalTime().isAfter(startTime) &&
object.getDateTime().toLocalTime().isBefore(endTime))
.collect(Collectors.toList());
Now let's look into the inner loop which calculates the total:
int total = 0;
for (ObjectA object : list) {
if (object.getDateTime().toLocalDate().equals(objectA.getDateTime().toLocalDate())) {
total += object.getQuantity();
}
}
It's also stream-filter-collect sequence, but here you want to collect the sum. So you may use IntStream here which already has the sum() method:
int total = list.stream()
.filter(object -> object.getDateTime().toLocalDate().equals(
objectA.getDateTime().toLocalDate())
.mapToInt(ObjectA::getQuantity).sum();
To make your code less crowded I would extract this to the separate method:
private static int getQuantityByDate(List<ObjectA> list, LocalDate date) {
return list.stream().filter(object -> object.getDateTime().toLocalDate().equals(date))
.mapToInt(ObjectA::getQuantity).sum();
}
Now the next if statement. It just changes the last boolean argument, so I would rewrite it (even without Stream API):
objectBList.add(new ObjectB(objectA.getDateTime(), objectA.getDescription(),
objectA.getQuantity(), total > quantity));
So now we see that the outer loop becomes stream-map-collect chain and could be rewritten this way:
List<ObjectB> objectBList = objectAList.stream()
.map(objectA ->
new ObjectB(objectA.getDateTime(), objectA.getDescription(), objectA.getQuantity(),
getQuantityByDate(list, objectA.getDateTime().toLocalDate()) > quantity))
.collect(Collectors.toList());
Now you can notice that collecting into objectAList is unnecessary as we just use it to create another stream. So we can merge both loops into single pipeline, resulting in the following final code:
private static int getQuantityByDate(List<ObjectA> list, LocalDate date) {
return list.stream().filter(object -> object.getDateTime().toLocalDate().equals(date))
.mapToInt(ObjectA::getQuantity).sum();
}
public static List<ObjectB> getFilteredList(
List<ObjectA> list, LocalTime startTime, LocalTime endTime, int quantity) {
return list.stream()
.filter(object -> object.getDateTime().toLocalTime().isAfter(startTime) &&
object.getDateTime().toLocalTime().isBefore(endTime))
.map(objectA -> new ObjectB(
objectA.getDateTime(), objectA.getDescription(), objectA.getQuantity(),
getQuantityByDate(list, objectA.getDateTime().toLocalDate()) > quantity))
.collect(Collectors.toList());
}
I have some very simple code that I'm trying to get running marginally quicker (there are a lot of these small types of call dotted around the code which seems to be slowing things down) using LINQ instead of standard code.
The problem is this - I have a variable outside of the LINQ which the result of the LINQ query needs to add it.
The original code looks like this
double total = 0
foreach(Crop c in p.Crops)
{
if (c.CropType.Type == t.Type)
total += c.Area;
}
return total;
This method isn't slow until the loop starts getting large, then it slows on the phone. Can this sort of code be moved to a relatively quick and simple piece of LINQ?
Looks like you could use sum: (edit: my syntax was wrong)
total = (from c in p.Crops
where c.CropType.Type == t.Type
select c.Area).Sum();
Or in extension method format:
total = p.Crops.Where(c => c.CropType.Type == t.Type).Sum(c => c.area);
As to people saying LINQ won't perform better where is your evidence? (The below is based on post from Hanselman? I ran the following in linqpad: (You will need to download and reference nbuilder to get it to run)
void Main()
{
//Nbuilder is used to create a chunk of sample data
//http://nbuilder.org
var crops = Builder<Crop>.CreateListOfSize(1000000).Build();
var t = new Crop();
t.Type = Type.grain;
double total = 0;
var sw = new Stopwatch();
sw.Start();
foreach(Crop c in crops)
{
if (c.Type == t.Type)
total += c.area;
}
sw.Stop();
total.Dump("For Loop total:");
sw.ElapsedMilliseconds.Dump("For Loop Elapsed Time:");
sw.Restart();
var result = crops.Where(c => c.Type == t.Type).Sum(c => c.area);
sw.Stop();
result.Dump("LINQ total:");
sw.ElapsedMilliseconds.Dump("LINQ Elapsed Time:");
sw.Restart();
var result2 = (from c in crops
where c.Type == t.Type
select c.area).Sum();
result.Dump("LINQ (sugar syntax) total:");
sw.ElapsedMilliseconds.Dump("LINQ (sugar syntax) Elapsed Time:");
}
public enum Type
{
wheat,
grain,
corn,
maize,
cotton
}
public class Crop
{
public string Name { get; set; }
public Type Type { get; set; }
public double area;
}
The results come out favorably to LINQ:
For Loop total: 99999900000
For Loop Elapsed Time: 25
LINQ total: 99999900000
LINQ Elapsed Time: 17
LINQ (sugar syntax) total: 99999900000
LINQ (sugar syntax) Elapsed Time: 17
The main way to optimize this would be changing p, which may or may not be possible.
Assuming p is a P, and looks something like this:
internal sealed class P
{
private readonly List<Crop> mCrops = new List<Crop>();
public IEnumerable<Crop> Crops { get { return mCrops; } }
public void Add(Crop pCrop)
{
mCrops.Add(pCrop);
}
}
(If p is a .NET type like a List<Crop>, then you can create a class like this.)
You can optimize your loop by maintaining a dictionary:
internal sealed class P
{
private readonly List<Crop> mCrops = new List<Crop>();
private readonly Dictionary<Type, List<Crop>> mCropsByType
= new Dictionary<Type, List<Crop>>();
public IEnumerable<Crop> Crops { get { return mCrops; } }
public void Add(Crop pCrop)
{
if (!mCropsByType.ContainsKey(pCrop.CropType.Type))
mCropsByType.Add(pCrop.CropType.Type, new List<Crop>());
mCropsByType[pCrop.CropType.Type].Add(pCrop);
mCrops.Add(pCrop);
}
public IEnumerable<Crop> GetCropsByType(Type pType)
{
return mCropsByType.ContainsKey(pType)
? mCropsByType[pType]
: Enumerable.Empty<Crop>();
}
}
Your code then becomes something like:
double total = 0
foreach(Crop crop in p.GetCropsByType(t.Type))
total += crop.Area;
return total;
Another possibility that would be even faster is:
internal sealed class P
{
private readonly List<Crop> mCrops = new List<Crop>();
private double mTotalArea;
public IEnumerable<Crop> Crops { get { return mCrops; } }
public double TotalArea { get { return mTotalArea; } }
public void Add(Crop pCrop)
{
mCrops.Add(pCrop);
mTotalArea += pCrop.Area;
}
}
Your code would then simply access the TotalArea property and you wouldn't even need a loop:
return p.TotalArea;
You might also consider extracting the code that manages the Crops data to a separate class, depending on what P is.
This is a pretty straight forward sum, so I doubt you will see any benefit from using LINQ.
You haven't told us much about the setup here, but here's an idea. If p.Crops is large and only a small number of the items in the sequence are of the desired type, you could build another sequence that contains just the items you need.
I assume that you know the type when you insert into p.Crops. If that's the case you could easily insert the relevant items in another collection and use that instead for the sum loop. That will reduce N and get rid of the comparison. It will still be O(N) though.
I am looking for a pattern, algorithm, or library that will take a set of dates and return a description of the recurrence if one exits, i.e. the set [11-01-2010, 11-08-2010, 11-15-2010, 11-22-2010, 11-29-2010] would yield something like "Every Monday in November".
Has anyone seen anything like this before or have any suggestions on the best way to implement it?
Grammatical Evolution (GE) is suitable for this kind of problem, because you are searching for an answer that adheres to a certain language. Grammatical Evolution is also used for program generation, composing music, designing, etcetera.
I'd approach the task like this:
Structure the problem space with a grammar.
Construct a Context-free Grammar that can represent all desired recurrence patterns. Consider production rules like these:
datepattern -> datepattern 'and' datepattern
datepattern -> frequency bounds
frequency -> 'every' ordinal weekday 'of the month'
frequency -> 'every' weekday
ordinal -> ordinal 'and' ordinal
ordinal -> 'first' | 'second' | 'third'
bounds -> 'in the year' year
An example of a pattern generated by these rules is: 'every second and third wednesday of the month in the year 2010 and every tuesday in the year 2011'
One way to implement such a grammar would be through a class hierarchy that you will later operate on through reflection, as I've done in the example below.
Map this language to a set of dates
You should create a function that takes a clause from your language and recursively returns the set of all dates covered by it. This allows you to compare your answers to the input.
Guided by the grammar, search for potential solutions
You could use a Genetic algorithm or Simulated Annealing to match the dates to the grammar, try your luck with Dynamic Programming or start simple with a brute force enumeration of all possible clauses.
Should you go with a Genetic Algorithm, your mutation concept should consist of substituting an expression for another one based on the application of one of your production rules.
Have a look at the following GE-related sites for code and information:
http://www.bangor.ac.uk/~eep201/jge/
http://nohejl.name/age/
http://www.geneticprogramming.us/Home_Page.html
Evaluate each solution
The fitness function could take into account the textual length of the solution, the number of dates generated more than once, the number of dates missed, as well as the number of wrong dates generated.
Example code
By request, and because it's such an interesting challenge, I've written a rudimentary implementation of the algorithm to get you started. Although it works it is by no means finished, the design should definitively get some more thought, and once you have gleaned the fundamental take-aways from this example I recommend you consider using one the libraries I've mentioned above.
/// <summary>
/// This is a very basic example implementation of a grammatical evolution algorithm for formulating a recurrence pattern in a set of dates.
/// It needs significant extensions and optimizations to be useful in a production setting.
/// </summary>
static class Program
{
#region "Class hierarchy that codifies the grammar"
class DatePattern
{
public Frequency frequency;
public Bounds bounds;
public override string ToString() { return "" + frequency + " " + bounds; }
public IEnumerable<DateTime> Dates()
{
return frequency == null ? new DateTime[] { } : frequency.FilterDates(bounds.GetDates());
}
}
abstract class Bounds
{
public abstract IEnumerable<DateTime> GetDates();
}
class YearBounds : Bounds
{
/* in the year .. */
public int year;
public override string ToString() { return "in the year " + year; }
public override IEnumerable<DateTime> GetDates()
{
var firstDayOfYear = new DateTime(year, 1, 1);
return Enumerable.Range(0, new DateTime(year, 12, 31).DayOfYear)
.Select(dayOfYear => firstDayOfYear.AddDays(dayOfYear));
}
}
abstract class Frequency
{
public abstract IEnumerable<DateTime> FilterDates(IEnumerable<DateTime> Dates);
}
class WeeklyFrequency : Frequency
{
/* every .. */
public DayOfWeek dayOfWeek;
public override string ToString() { return "every " + dayOfWeek; }
public override IEnumerable<DateTime> FilterDates(IEnumerable<DateTime> Dates)
{
return Dates.Where(date => (date.DayOfWeek == dayOfWeek));
}
}
class MonthlyFrequency : Frequency
{
/* every .. */
public Ordinal ordinal;
public DayOfWeek dayOfWeek;
/* .. of the month */
public override string ToString() { return "every " + ordinal + " " + dayOfWeek + " of the month"; }
public override IEnumerable<DateTime> FilterDates(IEnumerable<DateTime> Dates)
{
return Dates.Where(date => (date.DayOfWeek == dayOfWeek) && (int)ordinal == (date.Day - 1) / 7);
}
}
enum Ordinal { First, Second, Third, Fourth, Fifth }
#endregion
static Random random = new Random();
const double MUTATION_RATE = 0.3;
static Dictionary<Type, Type[]> subtypes = new Dictionary<Type, Type[]>();
static void Main()
{
// The input signifies the recurrence 'every first thursday of the month in 2010':
var input = new DateTime[] {new DateTime(2010,12,2), new DateTime(2010,11,4),new DateTime(2010,10,7),new DateTime(2010,9,2),
new DateTime(2010,8,5),new DateTime(2010,7,1),new DateTime(2010,6,3),new DateTime(2010,5,6),
new DateTime(2010,4,1),new DateTime(2010,3,4),new DateTime(2010,2,4),new DateTime(2010,1,7) };
for (int cTests = 0; cTests < 20; cTests++)
{
// Initialize with a random population
int treesize = 0;
var population = new DatePattern[] { (DatePattern)Generate(typeof(DatePattern), ref treesize), (DatePattern)Generate(typeof(DatePattern), ref treesize), (DatePattern)Generate(typeof(DatePattern), ref treesize) };
Run(input, new List<DatePattern>(population));
}
}
private static void Run(DateTime[] input, List<DatePattern> population)
{
var strongest = population[0];
int strongestFitness = int.MinValue;
int bestTry = int.MaxValue;
for (int cGenerations = 0; cGenerations < 300 && strongestFitness < -100; cGenerations++)
{
// Select the best individuals to survive:
var survivers = population
.Select(individual => new { Fitness = Fitness(input, individual), individual })
.OrderByDescending(pair => pair.Fitness)
.Take(5)
.Select(pair => pair.individual)
.ToArray();
population.Clear();
// The survivers are the foundation for the next generation:
foreach (var parent in survivers)
{
for (int cChildren = 0; cChildren < 3; cChildren++)
{
int treeSize = 1;
DatePattern child = (DatePattern)Mutate(parent, ref treeSize); // NB: procreation may also be done through crossover.
population.Add((DatePattern)child);
var childFitness = Fitness(input, child);
if (childFitness > strongestFitness)
{
bestTry = cGenerations;
strongestFitness = childFitness;
strongest = child;
}
}
}
}
Trace.WriteLine("Found best match with fitness " + Fitness(input, strongest) + " after " + bestTry + " generations: " + strongest);
}
private static object Mutate(object original, ref int treeSize)
{
treeSize = 0;
object replacement = Construct(original.GetType());
foreach (var field in original.GetType().GetFields())
{
object newFieldValue = field.GetValue(original);
int subtreeSize;
if (field.FieldType.IsEnum)
{
subtreeSize = 1;
if (random.NextDouble() <= MUTATION_RATE)
newFieldValue = ConstructRandomEnumValue(field.FieldType);
}
else if (field.FieldType == typeof(int))
{
subtreeSize = 1;
if (random.NextDouble() <= MUTATION_RATE)
newFieldValue = (random.Next(2) == 0
? Math.Min(int.MaxValue - 1, (int)newFieldValue) + 1
: Math.Max(int.MinValue + 1, (int)newFieldValue) - 1);
}
else
{
subtreeSize = 0;
newFieldValue = Mutate(field.GetValue(original), ref subtreeSize); // mutate pre-maturely to find out subtreeSize
if (random.NextDouble() <= MUTATION_RATE / subtreeSize) // makes high-level nodes mutate less.
{
subtreeSize = 0; // init so we can track the size of the subtree soon to be made.
newFieldValue = Generate(field.FieldType, ref subtreeSize);
}
}
field.SetValue(replacement, newFieldValue);
treeSize += subtreeSize;
}
return replacement;
}
private static object ConstructRandomEnumValue(Type type)
{
var vals = type.GetEnumValues();
return vals.GetValue(random.Next(vals.Length));
}
private static object Construct(Type type)
{
return type.GetConstructor(new Type[] { }).Invoke(new object[] { });
}
private static object Generate(Type type, ref int treesize)
{
if (type.IsEnum)
{
return ConstructRandomEnumValue(type);
}
else if (typeof(int) == type)
{
return random.Next(10) + 2005;
}
else
{
if (type.IsAbstract)
{
// pick one of the concrete subtypes:
var subtypes = GetConcreteSubtypes(type);
type = subtypes[random.Next(subtypes.Length)];
}
object newobj = Construct(type);
foreach (var field in type.GetFields())
{
treesize++;
field.SetValue(newobj, Generate(field.FieldType, ref treesize));
}
return newobj;
}
}
private static int Fitness(DateTime[] input, DatePattern individual)
{
var output = individual.Dates().ToArray();
var avgDateDiff = Math.Abs((output.Average(d => d.Ticks / (24.0 * 60 * 60 * 10000000)) - input.Average(d => d.Ticks / (24.0 * 60 * 60 * 10000000))));
return
-individual.ToString().Length // succinct patterns are preferred.
- input.Except(output).Count() * 300 // Forgetting some of the dates is bad.
- output.Except(input).Count() * 3000 // Spurious dates cause even more confusion to the user.
- (int)(avgDateDiff) * 30000; // The difference in average date is the most important guide.
}
private static Type[] GetConcreteSubtypes(Type supertype)
{
if (subtypes.ContainsKey(supertype))
{
return subtypes[supertype];
}
else
{
var types = AppDomain.CurrentDomain.GetAssemblies().ToList()
.SelectMany(s => s.GetTypes())
.Where(p => supertype.IsAssignableFrom(p) && !p.IsAbstract).ToArray();
subtypes.Add(supertype, types);
return types;
}
}
}
Hope this gets you on track. Be sure to share your actual solution somewhere; I think it will be quite useful in lots of scenarios.
If your purpose is to generate human-readable descriptions of the pattern, as in your "Every Monday in November", then you probably want to start by enumerating the possible descriptions. Descriptions can be broken down into frequency and bounds, for example,
Frequency:
Every day ...
Every other/third/fourth day ...
Weekdays/weekends ...
Every Monday ...
Alternate Mondays ...
The first/second/last Monday ...
...
Bounds:
... in January
... between 25 March and 25 October
...
There won't be all that many of each, and you can check for them one by one.
What I would do:
Create samples of the data
Use a clustering algorithm
Generate samples using the algorithm
Creating a fitness function to measure how well it correlates to the full data set. The clustering algorithm will come up with either 0 or 1 suggestions and you can meassure it against how well it fits in with the full set.
Elementate/merge the occurrence with the already found sets and rerun this algorithm.
Looking at that you may want to use either Simulated Annealing, or an Genetic Algorithm. Also, if you have the descriptions, you may want to compare the descriptions to generate a sample.
You could access the system date or system dateandtime and construct crude calendar points in memory based on the date and the day of the week as returned by the call or function result. Then use the number of days in relevant months to sum them and add on the number of days of the day variable in the input and/or access the calendar point for the relevant week starting sunday or monday and calculate or increment index forward to the correct day. Construct text string using fixed characters and insert the relevant variable such as the full name of the day of the week as required. There may be multiple traversals needed to obtain all the events of which the occurrences are to be displayed or counted.
First, find a sequence, if it exists:
step = {day,month,year}
period=0
for d = 1 to dates.count-1
interval(d,step)=datedifference(s,date(d),date(d+1))
next
' Find frequency with largest interval
for s = year downto day
found=true
for d = 1 to dates.count-2
if interval(d,s)=interval(d+1,s) then
found=false
exit for
end if
next
if found then
period=s
frequency=interval(1,s)
exit for
end if
next
if period>0
Select case period
case day
if frequency mod 7 = 0 then
say "every" dayname(date(1))
else
say "every" frequency "days"
end if
case month
say "every" frequency "months on day" daynumber(date(1))
case years
say "every" frequency "years on" daynumber(date(1)) monthname(date(1))
end select
end if
Finally, deal with "in November", "from 2007 to 2010" etc., should be obvious.
HTH
I like #arjen answer but I don't think there is any need for complex algorithm. This is so so simple. If there is a pattern, there is a pattern... therefore a simple algorithm would work. First we need to think of the types of patterns we are looking for: daily, weekly, monthly and yearly.
How to recognize?
Daily: there is a record every day
Weekly: there is a record every week
Monthly: there is a record every month
Yearly: there is a record every year
Difficult? No. Just count how many repetitions you have and then classify.
Here is my implementation
RecurrencePatternAnalyser.java
public class RecurrencePatternAnalyser {
// Local copy of calendars by add() method
private ArrayList<Calendar> mCalendars = new ArrayList<Calendar>();
// Used to count the uniqueness of each year/month/day
private HashMap<Integer, Integer> year_count = new HashMap<Integer,Integer>();
private HashMap<Integer, Integer> month_count = new HashMap<Integer,Integer>();
private HashMap<Integer, Integer> day_count = new HashMap<Integer,Integer>();
private HashMap<Integer, Integer> busday_count = new HashMap<Integer,Integer>();
// Used for counting payments before due date on weekends
private int day_goodpayer_ocurrences = 0;
private int day_goodPayer = 0;
// Add a new calendar to the analysis
public void add(Calendar date)
{
mCalendars.add(date);
addYear( date.get(Calendar.YEAR) );
addMonth( date.get(Calendar.MONTH) );
addDay( date.get(Calendar.DAY_OF_MONTH) );
addWeekendDays( date );
}
public void printCounts()
{
System.out.println("Year: " + getYearCount() +
" month: " + getMonthCount() + " day: " + getDayCount());
}
public RecurrencePattern getPattern()
{
int records = mCalendars.size();
if (records==1)
return null;
RecurrencePattern rp = null;
if (getYearCount()==records)
{
rp = new RecurrencePatternYearly();
if (records>=3)
rp.setConfidence(1);
else if (records==2)
rp.setConfidence(0.9f);
}
else if (getMonthCount()==records)
{
rp = new RecurrencePatternMonthly();
if (records>=12)
rp.setConfidence(1);
else
rp.setConfidence(1-(-0.0168f * records + 0.2f));
}
else
{
calcDaysRepetitionWithWeekends();
if (day_goodpayer_ocurrences==records)
{
rp = new RecurrencePatternMonthly();
rp.setPattern(RecurrencePattern.PatternType.MONTHLY_GOOD_PAYER);
if (records>=12)
rp.setConfidence(0.95f);
else
rp.setConfidence(1-(-0.0168f * records + 0.25f));
}
}
return rp;
}
// Increment one more year/month/day on each count variable
private void addYear(int key_year) { incrementHash(year_count, key_year); }
private void addMonth(int key_month) { incrementHash(month_count, key_month); }
private void addDay(int key_day) { incrementHash(day_count, key_day); }
// Retrieve number of unique entries for the records
private int getYearCount() { return year_count.size(); }
private int getMonthCount() { return month_count.size(); }
private int getDayCount() { return day_count.size(); }
// Generic function to increment the hash by 1
private void incrementHash(HashMap<Integer, Integer> var, Integer key)
{
Integer oldCount = var.get(key);
Integer newCount = 0;
if ( oldCount != null ) {
newCount = oldCount;
}
newCount++;
var.put(key, newCount);
}
// As Bank are closed during weekends, some dates might be anticipated
// to Fridays. These will be false positives for the recurrence pattern.
// This function adds Saturdays and Sundays to the count when a date is
// Friday.
private void addWeekendDays(Calendar c)
{
int key_day = c.get(Calendar.DAY_OF_MONTH);
incrementHash(busday_count, key_day);
if (c.get(Calendar.DAY_OF_WEEK) == Calendar.FRIDAY)
{
// Adds Saturday
c.add(Calendar.DATE, 1);
key_day = c.get(Calendar.DAY_OF_MONTH);
incrementHash(busday_count, key_day);
// Adds Sunday
c.add(Calendar.DATE, 1);
key_day = c.get(Calendar.DAY_OF_MONTH);
incrementHash(busday_count, key_day);
}
}
private void calcDaysRepetitionWithWeekends()
{
Iterator<Entry<Integer, Integer>> it =
busday_count.entrySet().iterator();
while (it.hasNext()) {
#SuppressWarnings("rawtypes")
Map.Entry pair = (Map.Entry)it.next();
if ((int)pair.getValue() > day_goodpayer_ocurrences)
{
day_goodpayer_ocurrences = (int) pair.getValue();
day_goodPayer = (int) pair.getKey();
}
//it.remove(); // avoids a ConcurrentModificationException
}
}
}
RecurrencePattern.java
public abstract class RecurrencePattern {
public enum PatternType {
YEARLY, MONTHLY, WEEKLY, DAILY, MONTHLY_GOOD_PAYER
}
public enum OrdinalType {
FIRST, SECOND, THIRD, FOURTH, FIFTH
}
protected PatternType pattern;
private float confidence;
private int frequency;
public PatternType getPattern() {
return pattern;
}
public void setPattern(PatternType pattern) {
this.pattern = pattern;
}
public float getConfidence() {
return confidence;
}
public void setConfidence(float confidence) {
this.confidence = confidence;
}
public int getFrequency() {
return frequency;
}
public void setFrequency(int frequency) {
this.frequency = frequency;
}
}
RecurrencePatternMonthly.java
public class RecurrencePatternMonthly extends RecurrencePattern {
private boolean isDayFixed;
private boolean isDayOrdinal;
private OrdinalType ordinaltype;
public RecurrencePatternMonthly()
{
this.pattern = PatternType.MONTHLY;
}
}
RecurrencePatternYearly.java
public class RecurrencePatternYearly extends RecurrencePattern {
private boolean isDayFixed;
private boolean isMonthFixed;
private boolean isDayOrdinal;
private OrdinalType ordinaltype;
public RecurrencePatternYearly()
{
this.pattern = PatternType.YEARLY;
}
}
Main.java
public class Algofin {
static Connection c = null;
public static void main(String[] args) {
//openConnection();
//readSqlFile();
RecurrencePatternAnalyser r = new RecurrencePatternAnalyser();
//System.out.println(new GregorianCalendar(2015,1,30).get(Calendar.MONTH));
r.add(new GregorianCalendar(2015,0,1));
r.add(new GregorianCalendar(2015,0,30));
r.add(new GregorianCalendar(2015,1,27));
r.add(new GregorianCalendar(2015,3,1));
r.add(new GregorianCalendar(2015,4,1));
r.printCounts();
RecurrencePattern rp;
rp=r.getPattern();
System.out.println("Pattern: " + rp.getPattern() + " confidence: " + rp.getConfidence());
}
}
I think you'll have to build it, and I think it will be a devil in the details kind of project. Start by getting much more thorough requirements. Which date patterns do you want to recognize? Come up with a list of examples that you want your algorithm to successfully identify. Write your algorithm to meet your examples. Put your examples in a test suite so when you get different requirements later you can make sure you didn't break the old ones.
I predict you will write 200 if-then-else statements.
OK, I do have one idea. Get familiar with the concepts of sets, unions, coverage, intersection and so on. Have a list of short patterns that you search for, say, "Every day in October", "Every day in November", and "Every day in December." If these short patterns are contained within the set of dates, then define a union function that can combine shorter patterns in intelligent ways. For example, let's say you matched the three patterns I mention above. If you Union them together you get, "Every day in October through December." You could aim to return the most succinct set of unions that cover your set of dates or something like that.
Have a look at your favourite calendar program. See what patterns of event recurrence it can generate. Reverse engineer them.